Prof. Yong-Hyuk Kim
School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
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Academic Career Length
20Years (2007-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 November 2018 in Computational Intelligence and Neuroscience
Journal Article
JSJae-Hyun Seo
Jae-Hyun SeoYong-Hyuk KimThe KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class. We define the U2R, R2L, and Probe classes, which are each less than 1% of the total dataset, as rare classes. In this study, we attempt to mitigate the class imbalance of the dataset. Using the synthetic minority oversampling technique (SMOTE), we attempted to optimize the SMOTE ratios for the rare classes (U2R, R2L, and Probe). After randomly generating a number of tuples of SMOTE ratios, these tuples were used to create a numerical model for optimizing the SMOTE ratios of the rare classes. The support vector regression was used to create the model. We assigned each instance in the test dataset to the model and chose the best SMOTE ratios. The experiments using machine-learning techniques were conducted using the best ratios. The results using the proposed method were significantly better than those of previous approach and other related work.
6 January 2014 in Advances in Meteorology
Journal Article
We developed a method to predict heavy rainfall in South Korea with a lead time of one to six hours. We modified the AWS data for the recent four years to perform efficient prediction, through normalizing them to numeric values between 0 and 1 and undersampling them by adjusting the sampling sizes of no-heavy-rain to be equal to the size of heavy-rain. Evolutionary algorithms were used to select important features. Discriminant functions, such as support vector machine (SVM),k-nearest neighbors algorithm (k-NN), and variantk-NN (k-VNN), were adopted in discriminant analysis. We divided our modified AWS data into three parts: the training set, ranging from 2007 to 2008, the validation set, 2009, and the test set, 2010. The validation set was used to select an important subset from input features. The main features selected wereprecipitation sensingandaccumulated precipitation for 24 hours. In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. The equitable treatment score of SVM with polynomial kernel was the highest among our experiments on average.k-VNN outperformedk-NN, but it was dominated by SVM with polynomial kernel.
13 August 2014 in The Scientific World Journal
Journal Article
YYYourim Yoon
Yourim Yoon
Yong-Hyuk KimA real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS and by 8% compared to a scheduling algorithm based on net power.
25 May 2021 in IEEE Transactions on Cybernetics
Journal Article
YYYourim Yoon
Yourim YoonYong-Hyuk KimWe derive the upper and lower bounds on the coverage of a 2-D deployment of static sensors. We use these bounds in constructing a method of estimating the coverage of deployment by assuming that there are only pairwise intersections between the disks representing the range of each sensor. The speed of this approximation allows it to be built into a local search technique, as part of a memetic algorithm (MA) that tries to deploy a given set of sensors with maximum coverage. We show that this MA outperforms the previous techniques in terms of both speed and coverage achieved.
Journal Article
JKjingeun kim
jingeun kimYYYourim Yoon
Yourim YoonHPHye-Jin Park
Hye-Jin ParkYong-Hyuk KimMicroarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN), and the resulting accuracies were compared. k-fold cross-validation was used in evaluating the performance and the result was analyzed by comparing the performances of the five machine learning methods. Through the experiments, it was observed that the two tree-based methods, DT and RF, showed similar trends in results and the remaining three methods, MLP, SVM, and DT, showed similar trends. DT and RF generally showed worse performance than other methods except for one dataset. This suggests that, for the effective classification of microarray data, selecting a classification algorithm that is suitable for data traits is crucial to ensure optimum performance.
About Me
Yong-Hyuk Kim received a B.S. degree in Computer Science and M.S. and Ph.D. degrees in Computer Science and Engineering from Seoul National University (SNU), Seoul, South Korea, in 1999, 2001, and 2005, respectively. From 2005 to 2007, he was a Postdoctoral Scholar at SNU and a Research Staff Member at the Inter-University Semiconductor Research Center at SNU. Since 2007, he has been a Professor in the School of Software at Kwangwoon University, Seoul. He was also a Visiting Researcher at the Forecast Research Laboratory of the National Institute of Meteorological Research, Seoul, in 2013. From 2021 to 2022, he was a Visiting Professor in the School of Medicine and Public Health at the University of Wisconsin–Madison. His current research interests include algorithm design and analysis, discrete mathematics, optimization theory, combinatorial optimization, evolutionary computation, operations research, data/web mining, machine learning, smart grids, and sensor networks. Dr. Kim served as an Editor for the KSII Transactions on Internet and Information Systems from 2010 to 2013. He was also a Lead Guest Editor for special issues in DDNS (2015–2016), Mathematics (2021), AIMS Mathematics (2024), and Discover Artificial Intelligence (2025). He has served as a Committee Member for GECCO in 2005, 2006, and 2013, and for IEEE CEC from 2009 to 2012.